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PyTorch is a Python package that provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration. Deep neural networks built on a tape-based autograd system. You can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.
The torch package contains data structures for multi-dimensional tensors and defines mathematical operations over these tensors. Additionally, it provides many utilities for efficient serialization of Tensors and arbitrary types, and other useful utilities.
PyTorch 2.4 adds Python 3.12 support for torch.compile, FSDP2, custom ops API, and optimizations for AWS Graviton and GenAI workloads on CPUs. Learn more
PyTorch is an optimized tensor library for deep learning using GPUs and CPUs. Features described in this documentation are classified by release status: Stable: These features will be maintained long-term and there should generally be no major performance limitations or gaps in documentation.
PyTorch is a Python package that provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration. Deep neural networks built on a tape-based autograd system. You can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.
Feb 27, 2024 · PyTorch is an open-source machine learning library for Python developed by Facebook’s AI Research Lab (FAIR). It is widely used for building deep learning models and conducting research in various fields like computer vision, natural language processing, and reinforcement learning.
PyTorch is the premier open-source deep learning framework developed and maintained by Facebook. At its core, PyTorch is a mathematical library that allows you to perform efficient computation and automatic differentiation on graph-based models.
